The delivery of precision medicine is a primary objective for both clinical and translational investigators. Patients with newly diagnosed prostate cancer (PCa) face the challenge of deciding among multiple initial treatment modalities. The purpose of this study is to utilize artificial neural network (ANN) modeling to predict survival outcomes according to initial treatment modality and to develop an online decision-making support system.
Data were collected retrospectively from 7267 patients diagnosed with PCa between January 1988 and December 2017. The analyses included 19 pretreatment clinicopathological covariates. Multilayer perceptron (MLP), MLP for N-year survival prediction (MLP-N), and long short-term memory (LSTM) ANN models were used to analyze progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS), according to initial treatment modality. The performances of the ANN and the Cox-proportional hazards regression models were compared using Harrell's C-index.
The ANN models provided higher predictive power for 5- and 10-year progression to CRPC-free survival, CSS, and OS compared to the Cox-proportional hazards regression model. The LSTM model achieved the highest predictive power, followed by the MLP-N, and MLP models. We developed an online decision-making support system based on the LSTM model to provide individualized survival outcomes at 5 and 10 years, according to the initial treatment strategy.
The LSTM ANN model may provide individualized survival outcomes of PCa according to initial treatment strategy. Our online decision-making support system can be utilized by patients and health-care providers to determine the optimal initial treatment modality and to guide survival predictions.
World journal of urology. 2020 Jan 10 [Epub ahead of print]
Kyo Chul Koo, Kwang Suk Lee, Suah Kim, Choongki Min, Gyu Rang Min, Young Hwa Lee, Woong Kyu Han, Koon Ho Rha, Sung Joon Hong, Seung Choul Yang, Byung Ha Chung
Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea., Selvas AI, Seoul, Republic of Korea., Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea. .